How does Atlas find duplicated Polars code if variable names are different?

Atlas uses hybrid semantic and keyword retrieval, fused by reciprocal rank fusion, and indexes code by AST declarations, allowing it to identify semantically similar Polars logic even when variable names or minor syntax differ.

Can Atlas run my existing `pytest` suite for Polars?

Yes, Atlas can execute `pytest` commands via its `bash` tool, allowing you to run your `pytest (assert_frame_equal)` suite after each refactoring step to verify correctness in your Polars project.

How does Atlas ensure my Polars code stays formatted?

Atlas integrates with your existing toolchain. After making changes, you can instruct Atlas to run `ruff format` via the `bash` tool, ensuring your Polars code adheres to your project's formatting standards.

What if Atlas makes a mistake when refactoring my Polars code?

Atlas operates with explicit permission. It drafts a plan, shows unified diffs for every file edit, and requires your approval before writing. You can review and roll back changes using git patches, ensuring full control over your Polars codebase.

Does Atlas support Polars' lazy execution model?

Yes, Atlas is designed to understand Polars' expression API and lazy query optimizer. It can read your `LazyFrame` chains and expression contexts, helping you refactor without prematurely calling `.collect()` and preserving predicate pushdown.

How does Atlas handle dependencies in my Polars project?

Atlas works within your existing project setup. It expects a `pyproject.toml` that pins Polars and can use your `uv` package manager commands via the `bash` tool for any dependency-related tasks, just as you would manually.

Is Atlas aware of Polars-specific idioms like predicate pushdown?

Yes, Atlas can be instructed to convert operations like `scan_csv` plus a filter into a lazy chain, ensuring predicate pushdown reaches the reader. It can also print `explain()` on query plans to show which projections got pruned before and after.

Extract a Shared Helper from Duplicated Polars Code with Atlas in 2026

In 2026, Polars developers can efficiently extract shared helpers from duplicated code using Atlas, which leverages semantic search to identify similar logic, then refactors it with `write` and `apply_patch`, ensuring correctness with `pytest (assert_frame_equal)` and formatting with `ruff format`.

How do I find duplicated Polars logic that grep misses?

Identifying duplicated Polars logic, even when variable names differ, is a core refactoring task in 2026. Atlas's `codebase_search` tool uses hybrid semantic and keyword retrieval, fused by reciprocal rank fusion, to surface near-duplicate implementations that traditional text-based tools like `grep` would miss across your Polars project.

Polars' expression API and lazy query optimizer often lead to similar logical constructs that appear textually different due to varying variable names or slight structural changes. Traditional tools like `grep` struggle with this semantic duplication. Atlas's `codebase_search` tool overcomes this limitation by indexing your code using AST declarations via tree-sitter, rather than blind line windows. This allows Atlas to understand the underlying structure and meaning of your Polars code. By leveraging local Ollama embeddings, Atlas can perform a deep semantic search, identifying code blocks that perform the same job, even if their surface-level syntax or variable names diverge. This capability is crucial for Polars developers aiming to consolidate redundant `LazyFrame` chains or `Expr` definitions into a single, maintainable helper.

How do I create a new shared Polars helper file?

After identifying duplicated Polars logic, creating a new shared helper module is the next step. Atlas's `write` tool allows you to draft and create new Python files, such as `src/polars_helpers.py`, presenting a full unified diff for your approval before any changes are written to disk, ensuring you have 100% control over the new module's content.

Once you have confirmed the semantic equivalence of duplicated Polars code, Atlas's `write` tool enables you to create a new Python file to house your shared helper. For instance, you might instruct Atlas to create `src/polars_helpers.py`. Before the file is actually created, Atlas will present a comprehensive unified diff, showing exactly what content will be written. This permission-gated step ensures that you, the Polars developer, have complete oversight and can approve or modify the proposed helper function. This new module can contain functions that accept and return Polars `LazyFrame` or `Expr` objects, adhering to Polars' best practices by avoiding premature `.collect()` calls and leveraging the lazy execution model.

How do I replace duplicated Polars code with a helper call?

Replacing each instance of duplicated Polars code with a call to your new shared helper is a critical step in refactoring. Atlas's `apply_patch` tool facilitates this by generating and applying a unified diff for each file, allowing you to review and approve each change individually, ensuring precise modifications across your 2026 Polars codebase.

With your new Polars helper function defined, the next task is to replace all instances of the duplicated logic with calls to this helper. Atlas's `apply_patch` tool is designed for this precise operation. It generates a distinct unified diff for each file where a replacement is needed. This granular approach means that each swap is independently reviewable and revertible, providing a high degree of safety and control. For example, if you have a `LazyFrame` chain duplicated in `src/data_processing.py` and `src/reporting.py`, Atlas will propose two separate patches, one for each file. You can use Atlas's `read` tool to inspect the changes before approving them, ensuring that the new helper call correctly integrates into your existing Polars data pipelines.

How do I test and format Polars code after refactoring?

Maintaining code quality and correctness is paramount when refactoring Polars code. After each replacement, Atlas can run your test suite using `bash -c 'pytest tests/my_module_test.py'` and format the modified files with `ruff format src/my_module.py`, ensuring that your changes adhere to established standards and that no regressions are introduced in your 2026 project.

After each `apply_patch` operation, it is crucial to verify that the refactoring has not introduced any regressions. Atlas's `bash` tool allows you to execute arbitrary shell commands, including your Polars test suite. You can instruct Atlas to run `bash -c 'pytest tests/my_module_test.py'` to execute relevant tests. For Polars code, `pytest (assert_frame_equal)` is the standard for comparing `DataFrame` or `LazyFrame` outputs, ensuring data integrity. Additionally, to maintain code consistency, Atlas can run `bash -c 'ruff format src/my_module.py'` on the modified files. This ensures that your refactored Polars code remains well-formatted according to your project's `ruff` configuration. Finally, after all replacements, a final `grep` for any surviving copies of the original logic confirms a complete refactor.

What safety features does Atlas offer for Polars refactoring?

Atlas provides robust safety and review mechanisms for Polars refactoring, ensuring developers maintain full control over their codebase in 2026. Every Atlas tool call is permission-gated against allow, ask, and deny rules, and all file edits are presented as unified diffs for explicit approval, preventing unintended modifications to your critical Polars logic.

Atlas is built with developer control and safety as a top priority, especially when dealing with complex refactoring tasks in Polars. Before any tool call is executed, it passes through a permission-gated system, allowing you to explicitly allow, ask for confirmation, or deny its execution. When Atlas drafts a plan, it does so in a read-only plan agent, only switching to a build agent after your approval. Crucially, every file edit, whether creating a new helper with `write` or applying a patch with `apply_patch`, is presented as a unified diff for your review and explicit approval. Atlas also reads git branches, status, and diffs, and can snapshot file changes as git patches, allowing you to easily diff and roll back any edits, providing an unparalleled safety net for your Polars codebase.

Step by step

01Start Atlas in your Polars project, ensuring your `pyproject.toml` pins Polars.
02Ask Atlas's `codebase_search` tool for the behavior of the duplicated Polars logic to surface near-duplicate implementations that `grep` would miss.
03Use Atlas's `read` tool to inspect each search hit and confirm the Polars code copies are genuinely equivalent before collapsing them.
04Create the new shared Polars helper file, for example `src/polars_helpers.py`, using Atlas's `write` tool, reviewing the full diff in the permission prompt.
05For each instance of duplicated Polars code, use Atlas's `apply_patch` tool to replace it with a call to the new helper, reviewing each file's patch independently.
06After every `apply_patch` operation, run your Polars test suite with Atlas's `bash -c 'pytest tests/my_module_test.py'` to ensure correctness using `assert_frame_equal`.
07Format the modified Polars files with Atlas's `bash -c 'ruff format src/my_module.py'` to maintain consistent code style.
08Finish by using Atlas's `grep` tool to search for any surviving copies of the original Polars logic, confirming a complete refactor.

Frequently asked questions

How does Atlas find duplicated Polars code if variable names are different?: Atlas uses hybrid semantic and keyword retrieval, fused by reciprocal rank fusion, and indexes code by AST declarations, allowing it to identify semantically similar Polars logic even when variable names or minor syntax differ.
Can Atlas run my existing `pytest` suite for Polars?: Yes, Atlas can execute `pytest` commands via its `bash` tool, allowing you to run your `pytest (assert_frame_equal)` suite after each refactoring step to verify correctness in your Polars project.
How does Atlas ensure my Polars code stays formatted?: Atlas integrates with your existing toolchain. After making changes, you can instruct Atlas to run `ruff format` via the `bash` tool, ensuring your Polars code adheres to your project's formatting standards.
What if Atlas makes a mistake when refactoring my Polars code?: Atlas operates with explicit permission. It drafts a plan, shows unified diffs for every file edit, and requires your approval before writing. You can review and roll back changes using git patches, ensuring full control over your Polars codebase.
Does Atlas support Polars' lazy execution model?: Yes, Atlas is designed to understand Polars' expression API and lazy query optimizer. It can read your `LazyFrame` chains and expression contexts, helping you refactor without prematurely calling `.collect()` and preserving predicate pushdown.
Can I review each change Atlas makes to my Polars files?: Absolutely. Atlas's `apply_patch` tool replaces duplicates one file per patch, ensuring each swap is independently reviewable and revertible. Every file edit is presented as a unified diff for your explicit approval before writing.
How does Atlas handle dependencies in my Polars project?: Atlas works within your existing project setup. It expects a `pyproject.toml` that pins Polars and can use your `uv` package manager commands via the `bash` tool for any dependency-related tasks, just as you would manually.
Is Atlas aware of Polars-specific idioms like predicate pushdown?: Yes, Atlas can be instructed to convert operations like `scan_csv` plus a filter into a lazy chain, ensuring predicate pushdown reaches the reader. It can also print `explain()` on query plans to show which projections got pruned before and after.

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